There was a very strong agreement overall between all the algorithmic measurements of epithelial percentage made on the FOVs of a whole virtual slide and the subjective measurements of the same made by the two raters (Table 1).

In order to compare the manual, algorithmic, and robotic TMAs, we began with algorithmic measurements of epithelial percentage made on the FOVs of a whole virtual slide. The slide coordinates of the three FOVs with the highest epithelial percentage (as determined by the algorithms) were used to guide both manual construction of the TMA (algorithmic TMA) as well as construction by TMAker (robotic TMA) (Supplementary Material; “Demonstration of TMAker” section). The cores produced in both the algorithmic TMA and robotic TMA reflected high cancer cell density. Algorithmic TMA cores are depicted (Figure 2, row 3). In contrast, manual TMA based on subjective slide template alignment and manual construction often produced cores with comparatively low epithelial percentages and low cancer cell densities (Figure 2, row 4). These cores would be considered less informative.

The results of our comparative study of manual, algorithmic, and robotic TMA showed that algorithmic TMA contained cores with epithelial cell (cancer cell) percentages which were ~50% higher overall in cancer cell density than those of manual TMA. This observation held true for breast (P < 0.01), colon (P < 0.01), and lung (P < 0.001) TMAs (Table 2). What makes this comparison even more impressive is that the manual TMA had the first sampling of the tissue block, yet the algorithmic TMA produced cores of a higher epithelial (cancer cell) density. Robotic TMA showed a similar increase in overall cancer cell density compared with manual TMA (Figure 3, A–C). These observations held true across different TMAs: breast (P < .01), colon (P < .001), and lung (P < .001) (Table 3). The robotic TMA had an essentially equivalent epithelial (cancer cell) density to the algorithmic TMA (data not shown), but produced a TMA of even rows and columns of cores (Figure 3D).

We then compared the depth of sections of the TMA cores created by manual, algorithmic, and robotic TMA. At various levels—superficial, midway, and deep (nearly full thickness)— into the TMA block, there was a strong agreement among the epithelial percentages of all three depths for both manual and algorithmic TMA (Table 4, A and B). However, the increase in overall epithelial cell (cancer cell) percentages in the algorithmic TMA versus manual TMA held when compared at the various depths (Table 5). Results for the robotic TMA were similar to the results for the algorithmic TMA (data not shown). In all of these studies, the measurements of epithelial cell (cancer cell) percentages on both the whole slide FOVs as well as the final TMA virtual cores were algorithmically determined. However, there was also a very strong agreement overall between algorithmic epithelial cell (cancer cell) percentage measurements made on the virtual TMAs and the subjective measurements of the same made by the two raters (data not shown) just as there had been with the whole slide FOV comparisons (Table 1).

Our ERAs were not perfect and there were some examples of both false negative and false positive epithelial recognition. Infiltrating lobular carcinomas of the breast, because they consist of small epithelial cells, sometimes could not be detected adequately and gave rise to false negative epithelial recognition. Some lymphoid aggregates, because they consisted of cellular clumps, were often considered by the algorithm to be epithelial in nature and gave rise to false positive epithelial recognition. A weblink demonstration of the ERAs showing both true positivity and true negativity, as well as false negativity and false positivity, is provided (http://www.pathxchange.org). Details to access this link are provided (Supplementary Materials; “Demonstration of web site link” section).